The automatic number plate recognition systems (ANPR) exist for a long time, but only in the late 90s it became an important application because of the large increase in the number of vehicles. The information extracted from the license plates is mainly used for traffic monitoring, access control, parking, motorway road tolling, and border control, making car logs for parking systems, journey time measurementetc. by the law enforcement agencies.
The recognition problem is generally sub-divided into 5 parts:
(1) Image acquisition: Capturing the image of the license plate
(2) Pre-processing the image:Normalization, adjusting the brightness, skewness and contrast of the image
(3) Localising the license plate : Extracting the number plate.
(4) Character segmentation : Locating and identifying the individual symbol images on the plate.
(5) Optical character recognition: There may be further refinements over these (like matching the vehicle license number with a particular database to track suspected vehicles etc.) but the basic structure remains the same.
A guiding parameter in this regard is country-specific traffic norms and standards. This helps to fine tune the system i.e. number of characters in the license plate, text luminance level (relative index i.e. dark text on light background or light text on dark background) etc. So the problem can then be narrowed down for application in a particular country. For example, in India the norm is printing the license plate numbers in black colour on a white background for private vehicles and on a yellow background for commercial vehicles. The general format for the license plate is two letters (for state code) followed by district code, then a four digit code specific to a particular vehicle.
ANPR systems as a practical application of artificial intelligence
Massive integration of information technologies into all aspects of modern life caused demand for processing.